TL;DR
Experiments on a new approach for fine-grained evaluation demonstrate that this approach enhances a model’s ability to understand fine-grained differences.
It is shown that paragraph supervision enables effective use of long token sequences, whereas caption-only training degrades beyond 60 tokens, and paragraph supervision consistently benefits long-description benchmarks and hard negatives prove detrimental in text-only fine-tuning.
The Semantic-Aware Fine-Tuning (SAFT) framework is proposed to address semantic compression in specific domains, which incorporates Semantic-Aware Soft-Label Supervision and Intra-modal Structural Distillation to establish a promising paradigm for domain-specific TBIR tasks.
A new model based on a Large Multimodal Model (LMM) that functions as a context-aware reasoner for CoCo-IR is proposed, which interprets the entire interaction history to generate Transformable Image Embeddings (TIE) that evolve across turns.
An empirical study comparing two modular pipelines with SA-DBNet, a custom detector architecture combining ResNet-18 with self-attention spatial modeling and deformable convolutions against an end-to-end vision-language baseline, evaluated on 4013 degraded images with 7000 question-answer pairs finds that conventional...
An image may be worth a thousand words, but most captioning models describe it in only a few. Modern vision-language models produce fluent high-level captions, yet routinely miss the attributes, counts, textures, materials, and spatial relations that make an image visually specific. Recent multi-stage systems recover s...
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